· T. Singh, B. Benson, S. Raza, T. Kim, N. Pogorelov, W.P. Smith, C.N. Arge “Improving the Arrival Time Estimates of Coronal Mass Ejections by Using Magnetohydrodynamic Ensemble Modeling, Heliospheric Imager data, and Machine Learning”, Astro Physical Journal (ApJ), February. 2023. (Link)

· R. Hooda, W. D. Pan and B. Benson, “Transform Decomposition Switching for Efficient Attribute Compression of 3D Point Clouds Using Neural Networks”, in Proc. of 2022 International Conference on Computational Science and Computational Intelligence (CSCI'22), Las Vegas, NV, Dec. 2022. (Link) (Slides)

· T. Singh, B. Benson, S. Raza, T. Kim, N. Pogorelov, “Improving the Arrival Time Prediction of Coronal Mass Ejections using Magnetohydrodynamic Ensemble Modeling, Heliospheric Imager data and Machine Learning”, in Proc. of 2022 EGU General Assembly Conference Abstracts (EGU'22), Vienna, Austria, May. 2022. (Link)

· B. Benson, E. Brown, S. Bonasera, G. Acciarini, J. A. Pérez-Hernández, E. Sutton, M. K. Jah, C. P. Bridges, M. Jin and A. G. Baydin “Simultaneous Multivariate Forecast of Space WeatherIndices using Deep Neural Network Ensembles,” in Proc. of Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021), Vancouver, CA, Dec. 2021. (Link)

· E. Brown, S. Bonasera, B. Benson, J. A. Pérez-Hernández, G. Acciarini, A. G. Baydin, C. P. Bridges, M. Jin, E. Sutton and M. K. Jah, “Learning the solar latent space: sigma-variationalautoencoders for multiple channel solar imaging,” in Proc. of Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021), Vancouver, CA, Dec. 2021. (Link)

· S. Bonasera, G. Acciarini, J. A. Pérez-Hernández, B. Benson, E. Brown, E. Sutton, M. K. Jah, C. P. Bridges and A. G. Baydin “Dropout and Ensemble Networksfor Thermospheric Density Uncertainty Estimation,” in Proc. of Bayesian Deep Learning Workshop (NeurIPS 2021), Vancouver, CA, Dec. 2021. (Link)

· B. Benson, W. D. Pan, A. Prasad, G. A. Gary, and Q. Hu,“On the Estimation of the SHARP Parameter MEANALP from AIA Images Using Deep Neural Networks,” Solar Physics, vol. 296. no. 163, Nov 2021. (Springer Nature SharedIt Link)

· B. Benson, W. D. Pan, A. Prasad, G. A. Gary, and Q. Hu,“Forecasting Solar Cycle 25 Using Deep Neural Networks,” Solar Physics, vol. 295. no. 65, May 2020. (Springer Nature SharedIt Link)

· B. Benson, W. D. Pan, A. Prasad, G. A. Gary, and Q. Hu,“Determining the Parameter for the Linear Force-Free Magnetic Field Model with Multi-Dipolar Configurations Using Deep Neural Networks,” In Proc. of Machine Learning in Heliophysics, Amsterdam, September 2019. (Poster)

· B. Benson, W. D. Pan, G. A. Gary, Q. Hu, and T. Staudinger, “Determining the Parameter for the Linear Force-Free Magnetic Field Model with Multi-Dipolar Configurations Using Deep Neural Networks,”Astronomy and Computing (an Elsevier Journal), vol. 26, January 2019, pp. 50 - 60. (Link)

· B. Benson, Z. Jiang, W. D. Pan, G. A. Gary, Q. Hu, “Determination of Linear Force-Free Magnetic Field Constant Alpha Using Deep Learning,” in Proc. of 2017 International Conference on Computational Science and Computational Intelligence (CSCI'17), Las Vegas, NV, Dec. 2017. (Link) (Slides)